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Hireon - AI Resume Analyzer

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Hireon - AI Resume Analyzer

The problem

Most job seekers have no idea whether their CV will survive an ATS (Applicant Tracking System) filter. Professional review services cost between $50 and $200 per resume, so a lot of qualified candidates get silently rejected before a human ever sees their file.

My role

Solo. I handled design, frontend, AI integration, and deployment.

The approach

I built a browser-based tool that parses the candidate's PDF on the client, extracts its text, and sends structured prompts to Claude 3.7 Sonnet for an ATS-aware review. Feedback comes back in categorized sections (formatting, keyword coverage, clarity, impact) so the user gets actionable edits instead of a generic score.

Technical decisions

Zustand over Redux / Context

The app has a handful of cross-cutting pieces of state (uploaded file, parse result, analysis response, loading flags) and no server. Redux would be ceremony; prop-drilling through Context would re-render half the tree on every analysis update. Zustand gave me selector-based subscriptions in ~50 lines of boilerplate.

Claude 3.7 Sonnet instead of a cheaper model

Earlier Claude and GPT-3.5 class models produced generic 'make your resume stronger' feedback that felt worse than a Google search. Sonnet 3.7 was the first model I tested that consistently caught ATS-specific issues like missing keywords for the target role, odd column layouts, and inconsistent verb tenses.

Parse the PDF in the browser

Uploading the raw PDF to a server means handling storage, deletion, and privacy disclosures for a file that often contains a home address and phone number. Parsing client-side keeps the file on the user's machine. Only the extracted text goes to the model.

The stack

React + TypeScript for the UI, Tailwind for styling, Zustand for state, pdf.js for in-browser PDF text extraction, Claude 3.7 Sonnet via the Anthropic API for the analysis step. Deployed on Vercel.

The outcome

I shipped it as a free tool. I used it on my own CV, and iterated on the prompts after watching where it gave weak feedback on real resumes from friends who tested it.

What I learned

Prompt design is the product. The difference between a 'meh' analyzer and a useful one was almost entirely in how I structured the system prompt and forced the model to return categorized, specific, rewrite-style suggestions instead of vague advice.